Financial Development and the Quality of the Environment in Nigeria: An Application of Non-Linear ARLD Approach
Why this work is in the frame
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Bibliographic record
Abstract
The present study examines the asymmetric effect of financial development on the quality of environment in Nigeria from 1970 to 2018. The study employed the techniques of non-linear ARDL approach as well as Diks and Panchenko (2006) non-linear test of causality. A comprehensive index of financial development is constructed using PCA. The empirical outcomes of the study reveal that financial development in Nigeria impedes the quality of the environment. The government should encourage lenders to ease the funding for the energy sector and allocate financial resources for environment-friendly businesses rather than wasting them in consumer financing. Moreover, economic growth and FDI are positively and significantly related to carbon emissions. On this basis, the government should introduce environmentally friendly technologies that will help improve the quality of the environment, increase long-term sustainability, and save resources for generations to come. A key policy consequence of this study is also that the FDI inflow to pollution-intensive industries should be closely monitored.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it